processMI

Process Special Multiple Imputation Output

Regression modeling, testing, estimation, validation,
graphics, prediction, and typesetting by storing enhanced model design
attributes in the fit.  'rms' is a collection of functions that
assist with and streamline modeling.  It also contains functions for
binary and ordinal logistic regression models, ordinal models for
continuous Y with a variety of distribution families, and the Buckley-James
multiple regression model for right-censored responses, and implements
penalized maximum likelihood estimation for logistic and ordinary
linear models.  'rms' works with almost any regression model, but it
was especially written to work with binary or ordinal regression
models, Cox regression, accelerated failure time models,
ordinary linear models,	the Buckley-James model, generalized least
squares for serially or spatially correlated observations, generalized
linear models, and quantile regression.

Frank Harrell Jr

Regression Modeling Strategies

processMI function

<dl><dt>object</dt>
<dd>a fit object created by <code><a href="/link/Hmisc%3A%3Afit.mult.impute()?package=rms&version=6.9-0" data-mini-rdoc="rms::Hmisc::fit.mult.impute()">Hmisc::fit.mult.impute()</a></code></dd>
<dt>...</dt>
<dd>ignored</dd></dl>

Arguments

Author

processMI — processMI

<dl>

<dt>object</dt>
<dd>a fit object created by <code><a href='https://rdrr.io/pkg/Hmisc/man/transcan.html'>Hmisc::fit.mult.impute()</a></code></dd>


<dt>...</dt>
<dd>ignored</dd>

</dl>

processMI: processMI

Description

Usage

Value

Arguments

Author

Details

See Also